About the Journal

Machine Learning for Human Intelligence (MLHI) is a peer-reviewed, open-access journal recognized by leading academic and research institutions. MLHI publishes high-impact scholarly articles exploring the intersection of machine learning and human cognitive processes, emphasizing innovative methodologies and transformative applications in Artificial Intelligence (AI), Machine Learning (ML), Data Science, and related Computer Science disciplines. We welcome original, unpublished research that advances the understanding of intelligent systems in synergy with human reasoning, behavior, and decision-making.

MLHI encourages submissions in the following areas:

• Cognitive and behavioral modeling using machine learning to interpret human intelligence, perception, and decision-making.

• Adaptive learning systems enhancing human-machine collaboration, including explainable AI (XAI) and interactive intelligence.

• Neurosymbolic approaches bridging symbolic reasoning with data-driven learning for human-like problem-solving.

• Ethical and socially responsible AI, focusing on fairness, transparency, and the societal impact of intelligent systems.

• Human-centered applications in healthcare, education, psychology, and human-computer interaction, where ML augments human capabilities.

• Novel algorithms inspired by neuroscience, psychology, or linguistics to improve machine understanding of human intelligence.

• Artificial Intelligence (AI): Neural networks, knowledge representation, reasoning, and AI ethics.

• Machine Learning (ML): Supervised/unsupervised learning, reinforcement learning, deep learning, and generative models.

• Data Science: Big data analytics, statistical modeling, and scalable algorithms for data-driven intelligence.

• Human-Centric AI: Explainable AI (XAI), cognitive modeling, human-AI collaboration, and behavioral analysis.

• Interdisciplinary Applications: AI/ML in healthcare, robotics, NLP, computer vision, cybersecurity, IoT, and social sciences.

• Emerging Computing Paradigms: Quantum machine learning, neuromorphic computing, and bio-inspired algorithms.

Articles must present novel, unpublished research with rigorous methodology. Interdisciplinary studies bridging AI/ML with other fields (e.g., neuroscience, psychology, engineering, social science and more) are encouraged. Reviews, surveys, and replication studies will be considered if they provide significant insights. MLHI serves as a platform for researchers to explore how intelligent systems can augment human capabilities, address global challenges, and redefine the future of computing.